Landslide Hazards - Stillaguamish Tribe

Transcription

Landslide Hazards in the Stillaguamish basin:
A New Set of GIS Tools
Prepared For
The Stillaguamish Tribe of Indians
Natural Resource Department
by
Daniel J. Miller
M2 Environmental Services
3040 NW 57th Street
Seattle WA 98107
June 8, 2004
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June 8, 2004
TABLE OF CONTENTS
Introduction ................................................................................................................................ 1
Methods and results.................................................................................................................... 2
Strategy................................................................................................................................... 2
Data Sources........................................................................................................................... 4
Upslope Landslides ................................................................................................................ 7
Landslide Initiation............................................................................................................. 7
Landslide Runout ............................................................................................................. 11
Stream Side Landslides ........................................................................................................ 15
Applications.............................................................................................................................. 17
Landslide Hazards ................................................................................................................ 17
Watershed Processes ............................................................................................................ 18
Debris-Flow-Prone Channels ........................................................................................... 18
Sediment Yield ................................................................................................................. 19
Relationship to Fish Habitat ............................................................................................. 20
Conclusions .............................................................................................................................. 22
References ................................................................................................................................ 23
Landslide Hazards in the Stillaguamish basin:
INTRODUCTION
Landslide hazard maps provide one component of an integrated planning approach to
sustainable use of natural resources. Landslides are well-known as agents of destruction,
which are ignored at our peril (e.g., Turner and Schuster 1996), but are also recognized as
integral components of watershed landscapes and ecosystems (e.g., Swanson et al. 1988,
Naiman et al. 1992). Landslides contribute to the construction of riparian terraces and fans,
and to the supply of sediment that composes channel beds and floodplains (Benda 1990, Grant
and Swanson 1995). Consequently, the frequency, size, location, and history of landsliding
affect the types, quality, and heterogeneity of channel and riparian habitats found within a
basin (Reeves et al. 1995, Benda et al. 2004). It is also recognized that land management
activities can alter the frequency, magnitude, and location of landslide occurrences (Swanson
and Dryness 1975, Dragovich et al. 1993, Montgomery et al. 2000). Because current
management strategies seek to minimize human alterations of watershed dynamics,
management planning includes identification of landslide-prone areas, assessment of their
sensitivity to landuse practices, and evaluation of the hazard they pose to watershed resources
(Washington_Forest_Practices_Board 1997).
Landslide mapping can also aid in assessment efforts and in planning conservation and
restoration actions. For these applications, landsliding must be considered both in terms of
direct and indirect consequences. For example, when activities within a watershed lead to
alterations of valley-floor environments (e.g., cutting of riparian forests or conversion of
forests to other land uses), the consequences of landsliding can change even if areas prone to
landsliding are fully protected, because the channels that must eventually carry the sediment
provided by the landslides are changed. Delineation of landslide processes within a watershed
can thus aid a variety of efforts.
Hazard identification also relies on recognition of landslide processes and consequences, so
these two types of efforts are complementary. Indeed, the consequences of landsliding dictate
Landslide Hazards in the Stillaguamish Basin: A New Set of GIS Tools
June 8, 2004
the hazards they pose. Hazard assessments typically rely on recognition of relatively local
consequences, e.g., the potential for landslide delivery of sediment to a stream channel. The
role of position within the basin, of up- and down-stream landslide potential, of the condition
of adjacent and downstream riparian forests, and of watershed history (which may have
altered landslide frequency), although recognized as important for assessing the cumulative
effects of landsliding, are not routinely incorporated explicitly into assessments of landslide
hazards. This is due, in large part, to the lack of quantitative measures for describing these
factors. The lack of such measures also hinders conservation and restoration planning. Such
plans commonly involve detailed assessments of landslide processes, of stand distributions
and riparian condition, and of watershed history (e.g., Collins et al. 1994), but there are few
quantitative methods to account for interactions between these factors or to quantify the
effects of future changes.
This report describes and illustrates newly developed, GIS-based tools for quantifying
landslide hazards. The availability of extensive digital data sets for basin topography and land
cover offers an opportunity to design and test new measures of landslide attributes within the
context of watershed processes. These data, in conjunction with mapping from aerial
photographs and field surveys, can provide spatially distributed estimates of landslide
susceptibility and of routing pathways for landslide debris. Combined with channel and
habitat information, this type of information can help to place the consequences of landsliding
into a broader, habitat-based perspective. These GIS models serve as an assessment tool that
can provide traditional landslide hazard maps, but the information and processing ability they
provide can also be used to evaluate landslide processes and potential consequences for other
types of applications.
METHODS AND RESULTS
Strategy
The basic strategy is to correlate observed landslide locations to quantified landscape
attributes. For this project, I used attributes related to topography, geology, and land cover.
All data are recorded in a GIS database, so that model correlations can be updated as new data
are collected. Landscape attributes are derived from digital data sets and, for this project,
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landslide locations are based on mapping from aerial photographs, with a focus on landslide
types that may actively deliver sediment to stream channels.
For the Stillaguamish basin, I’ve divided these landslides into two broad categories, upslope
and streamside landslides, based on their proximity to a stream channel. Upslope landslides
tend to be much longer than they are wide, and include both short-runout “shallow-rapid”
landslides and long-runout debris flows (debris slides, spreads, and flows using Cruden and
Varnes’ (1996) Table 3-1). Streamside landslides tend to be more nearly equal in width and
length, commonly have arcuate headscarps, and terminate at the valley floor, typically at a
stream channel. I’ve characterized landslides into these two categories because streamside
landslides can form primarily in response to stream undercutting of adjacent banks (Miller
and Sias 1998), whereas upslope landslides are thought to occur primarily in response to porepressure gradients associated with subsurface flow (Iverson 2000). These two types of
landslides are treated separately, because the topographic, geologic, and land cover controls
on the triggering processes differ between them.
Landslide susceptibility is characterized point-by-point, or in this case, pixel-by-pixel over a
GIS raster file, in terms of the probability that the pixel is located within (or contains) a
landslide scar or deposit. These probabilities are derived from the inventory of mapped
landslide locations overlain on topographic, geologic, and land cover attributes. At any pixel,
the probability is estimated from the proportion of pixels in the basin with similar attributes
that contain mapped landslides. As a simple example, consider pixels classified in terms of
slope gradient. If we divided them into slope classes and found that 2% of those with
gradients between 70% and 80% contained mapped landslides, we would assign a probability
of 0.02 to pixels with slope values in that range, meaning that, on average, of every 100 pixels
in that slope class, two would contain a mapped landslide. I use a combination of attributes
here to account for factors in addition to slope gradient, but the idea is the same. With this
method, the values obtained reflect only the landslides included in the database. In this case,
landslides were mapped solely from 2001 aerial photographs, so the probabilities calculated
reflect landslides that appeared active at that time.
For upslope landslides, probabilities are calculated only for the uppermost point of the
landslide scar, which represents the initiation point of the landslide. Pixels are thereby
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assigned a probability of containing a landslide-initiation point. I also use the mapped extent
of landslide runout to estimate the probability that a landslide continues down slope. For this
calculation I use a slightly different strategy. For each mapped landslide, the runout length is
characterized in terms of topographic and land-cover attributes along the runout path. The
entire sample of landslide runout tracks is then used to estimate the probability that a landslide
continues down slope, pixel-by-pixel, in terms of the topographic and land-cover attributes
along the entire runout path starting from the initiating pixel. With this method, the
probability for delivery to a stream channel can be estimated for a landslide originating from
any, and every, pixel in the basin. Given that a digital coverage of the Stillaguamish basin
contains about 17 million 10-meter-square pixels, it is clear that such calculations require a
computer.
These methods provide empirical estimates of landslide susceptibility and of the potential for
down slope delivery. When coupled with a stream data set, channel reaches can be
characterized in terms of their susceptibility to landslide impacts. These methods can also be
used to estimate the probable surface area of active streamside landslides, and the probable
runout length for upslope landslides that deliver to stream channels, both of which correlate to
landslide volume (May 2002). These models can thereby be used to estimate sediment yields
from landsliding. Moreover, because landslide susceptibility and delivery potential are tied to
land cover, the change in sediment yield associated with a change in land cover can be
estimated (to the extent that the relationship between landslide susceptibility and delivery
potential, if any, can be resolved by the available data). This provides a quantitative means of
assessing effects of past and planned land-use activities.
Data Sources
Landslide mapping was done using:
•
2001 color aerial photographs, approximate scale 1:15,000
•
USGS digital orthophotographs, sampled at 1-meter resolution
(http://wagda.lib.washington.edu/data/), of various vintage (1989-1994)
•
Digital raster graphics (DRG) of the USGS 7.5-minute topographic maps
(http://wagda.lib.washington.edu/data)
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Landslide locations were digitized directly on screen using the orthophotographs and DRGs
as base maps. An example is shown in Figure 1.
Digital analyses used the following data sources:
•
10-meter USGS DEMs
•
1:100,000-scale digital geology for Washington State (www.dnr.wa.gov/geology);
Pt. Townsend and Sauk River quadrangles
•
Digital land-cover classification, Snohomish County, derived from 2001 Landsat images
(www.co.snohomish.wa.us/publicwk/swm/publications/200302LandCoverAsOfAug2001)
•
A roads coverage
Geologic units were grouped into two broad categories: deep, unconsolidated glacial
sediments (GLACIAL), and all others (BEDROCK). The deep, glacial sediments found in the
Stilliguamish valley are subject to certain landslide types that are not found in areas with
shallow soils overlying more competent substrates (e.g., bedrock or till). These types include
large translational block slides, as seen at the Steelhead Haven (Hazel) landslide along the
North Fork of the Stillaguamish (e.g., Miller and Sias 1998), and by headward-eroding
landslide complexes, as seen at the DeForest Creek and Gold Basin landslides.
Snohomish County derived eleven land-cover types in their classification of the 2001 Landsat
imagery (Purser et al. 2003):
1) Mature evergreen forest
2) Medium evergreen forest
3) Deciduous stands
4) Shrubs and small trees
5) Grass
6) Bare Ground
7) Medium-density development
8) High-density development
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9) Alpine Rock/Talus Slopes
10) Open water
11) Unknown (shade, cloud)
These classes were grouped into two broad categories for this analysis:
1) FORESTED, including classes 1 through 3 above, and
2) OPEN, including all other classes (4 through 11).
These groupings divide land-cover between those with differing potential controls on
landslide processes. Classes in the FORESTED group may contribute a greater degree of root
strength to effective soil cohesion than those in the OPEN group (Schmidt et al. 2001,
Roering et al. 2003), which could result in differing susceptibility to shallow soil failures
under these cover classes. Evapotranspiration rates are probably higher for the FORESTED
group, which can reduce groundwater recharge, so that potential effects on deep-seated
landslides also differ between these land-cover groupings (Miller and Sias 1998). Bank
erosion and channel widening are associated with loss of riparian forests (Collins et al. 1994,
Collins 1997), so there may also be differences in rates of stream-side landsliding between
these two cover groups as well.
Additional cover classes were also defined for areas within 30 meters of a mapped road.
Together, these groupings provided six geology and cover types for which landslide rates
were determined, shown in Table 1 and in Figure 2.
Table 1. Landscape Groups
Geology
GLACIAL
BEDROCK
OPEN
1
2
Land Cover
FORESTED
3
4
ROADS
5
6
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Upslope Landslides
The methods used for characterizing upslope landslide initiation and delivery probabilities are
described in the interim report submitted for this project and in Miller and Burnett (in review).
I will briefly describe aspects of the model here, with more detailed descriptions for methods
that are new or have changed.
Landslide Initiation
The potential for landslide initiation is evaluated in terms of a landslide density. This density
is calibrated as a function of topography separately for each geology + land cover group.
Topography is characterized using a topographic index function that incorporates potential
topographic controls on landslide susceptibility. In previous work, slope gradient (Hofmeister
and Miller 2003) and the SHALSTAB model of Montgomery and Dietrich (1994) have been
used as topographic indices (Miller et al. 2003). For the Stillaguamish basin, I found better
results using an index that uses both slope gradient and local topographic convergence (Shaw
and Johnson 1995), but does not use contributing area (as SHALSTAB does). Although
upslope landslides are observed on relatively planar slopes (based on the landslide inventory),
most are associated with topographic convergent areas, such as hollows (Reneau et al. 1990)
and along gullies and low-order channels (Millard 1999, Millard et al. 2002). Using the
infinite slope model as a guide, a factor-of-safety may be estimated as (Iverson 2000)
FS =
tan ϕ C −ψγ W tan ϕ
+
tan θ γ S Z sin θ cosθ
(1)
where φ is the friction angle of the soil, θ is gradient of the ground surface, C is soil cohesion,
Z is soil depth measured vertically, ψ is the pressure head of ground water (measured
vertically), γW is the unit weight of water, and γS the unit weight of soil. The factor-of-safety
varies with soil properties, with the groundwater flow field, and with topographic attributes. A
function proportional to FS, with soil properties held constant, can provide an indication of
topographic controls on landslide susceptibility and serve as a topographic index for this
analysis. The pressure head ψ may also vary with topography. Sub-surface groundwater flow
in shallow soils has been characterized using an approximation assuming surface-parallel
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flow, from which the pressure head may be estimated as a function of contributing area per
unit contour, surface gradient, and rainfall rate (O'Laughlin 1986). This is the approximation
used for SHALSTAB, although Iverson (2000) points out that it requires certain assumptions
that may be unrealistic. Other authors have looked at partial, rather than total, contributing
area as a better representation of transient groundwater flow during rainstorms (e.g., Iida
1999, Lancaster et al. 2001). In this case, local topographic convergence is still important, but
dependence on upslope contributing area is reduced. This matches observations for the
Stillaguamish basin, for which long, planar hillslopes with large upslope contributing areas do
not exhibit landsliding. Following Shaw and Johnson (Shaw and Johnson 1995), I assumed
that pressure head is proportional to local topographic convergence, defined as
ψ=
αPIN
B tan θ
(2)
Here PIN is the number of inflowing adjacent pixels, based on the flow algorithm described by
Tarboton (1997), and B is the contour length crossed by flow out of the pixel (also estimated
using Tarboton’s algorithm for flow direction). PIN/B thus provides a pixel-based measure of
local topographic convergence. Equation 2 assumes that groundwater flow velocity is
proportional to slope gradient and that flow depth is measured vertically, thus accounting for
the tanθ in the denominator. In Equation 2, α is a constant of proportionality. With Equation
2, Equation 1 may be rewritten as
FS ∝ I * =
1
tan θ

1
1 +
 cos 2 θ

γW

γS
 
1 
 P
a  b − MIN  IN ,   
 B tan θ a   
 
(3)
where a = α/Z and b = C/(αγWtanφ). Equation 3 requires specification of three constants,
(γW/γS), a, and b; all other quantities are determined solely from topography. These constants
can be set to values consistent with regional soil properties to provide a topographic index, I*,
that is a function of slope gradient and local topographic convergence, as illustrated in Figure
3. The precise values of the constants in Equation 3 are not particularly influential, since
landslide density is empirically correlated to I*, as described below.
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With Equation 3, a topographic index value can be calculated for every pixel in a DEM.
Digitized landslide locations are overlain on the resulting grid of index values. For each
landslide, the index value for the upper-most pixel containing the landslide track is taken as
an indication of topographic conditions at the landslide initiation point. Using the grid of
index values and the set of values for the mapped landslide initiation points, both the
proportion of basin area and the proportion of landslides can be plotted as functions of
increasing I*, as shown in Figure 4.
The relationship between landslide numbers and basin area shown in the lower graph of
Figure 4 provides a means of defining topographic controls on landslide density. The inverse
of the slope of the line can be defined as
f ( I *) =
 dn 
 
N
dI * = A dn = ρ dn
0
N da
da
 da 
 
 A
dI *
(4)
where dn is the change in the number of landslides associated with a change dI* of the
topographic index, N is the total number of landslides, da is the change in basin area
associated with a change dI*, and A is total area. The total number of landslides divided by
the total area defines the overall landslide density, ρ0. The numerator in Eqn. 4, (dn/N)/dI*, is
just the slope of the landslide curve at a given I* value in the upper graph of Figure 4 and the
denominator, (da/A)/dI*, is the slope of the area curve.
From Eqn. 4, we have
dn = ρ 0 fda ,
(5)
which in terms of gridded values becomes a sum over pixels:
∆n = ∑ ρ 0 fAP = ρ 0 AP ∑ f
(6)
where AP is the area of a single DEM pixel. The number of landslides, ∆n, found over any
portion of the basin can be estimated by summing the value of f associated with each pixel,
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multiplied by the mean landslide density. Thus, f acts as a weighting term that accounts for
effects of local topography on landslide susceptibility: it separates the influence of topography
from other factors that affect landslide density.
These other factors include effects of geology and land cover. To account for these, the
landslides and area associated with each of the six landscape groups defined earlier (Table 1)
can be separated in Eqn. 4:
f ( I *) = ρ 0
∑ ∆n
∑ ∆a
(7)
Over any increment of I*, the number of associated landslides is the sum of those in each
landscape group and the area is the sum of areas in each landslide group. Using Eqn. 6, we
can define ∆n independently for each group:
∆n = ρ i wT ∆a ,
(8)
where the subscript i refers to the ith group and wt refers to the topographic weighting term.
With Equation 8, a separate landslide density is defined for each landscape group.
Rearranging Eqn. 8 to obtain a corrected topographic weighting term gives
dai
wT ( I *) = f ( I *) ρ 0
∑ dI *
(9)
da
∑ ρ i dI *i
Values for f(I*) and da/dI* area estimated using quadratic fits over a moving centered
window. Values for wT(I*) and ρi are obtained by repeatedly solving Eqn. 9, updating the wT
and ρi values each time until no differences are found between iterations. The resulting
function is shown in Figure 5, plotted as a function of I*, and in Figure 6, shown over the
entire basin. These show landslide density increasing with decreasing I* values, indicating
more landslides associated with steeper, more convergent slopes, up to a point, beyond which
no landslides are observed, indicating perhaps slopes too steep to accumulate sufficient soil
for landsliding. With each iteration, a value for ρi is estimated from Eqn. 8:
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ρi =
June 8, 2004
n
(10)
AP ∑ wT
By plotting the sum of wT over pixels within a landslide group against the observed number of
landslides, ρi is obtained from the slope of the resulting line. Results for each landscape group
are shown in Figure 7 and below in Table 2.
Table 2. Upslope Landslide Density
Landscape Group
GLACIAL-OPEN
GLACIAL-FORESTED
GLACIAL-ROADS
BEDROCK-OPEN
BEDROCK-FORESTED
BEDROCK-ROADS
Density
(#/k2)
Number of
Landslides
1.52
0.08
1.73
0.85
0.19
2.08
5
28
2
15
80
22
The topographic weighting function, which is determined independently of geology or land
cover, multiplied by the appropriate density above, gives a spatially distributed estimate of
landslide density, based on the distribution of landslides mapped from the 2001 aerial photos.
This density serves as an estimate of landslide susceptibility that is responsive to land cover.
Landslide Runout
Runout distances of mapped upslope landslides are characterized in terms of slope gradient,
valley width, tributary junction angles, land cover, and cumulative runout length. As a
landslide, or debris flow, moves downslope, it can either erode (scour) material from the
surface over which it travels, thus adding to its volume, deposit material as it moves, thus
reducing its volume, or travel with no net scour or deposition (transitional flow). In the
original implementation of this model (Miller and Burnett, in review), the extent of scour,
transitional flow, and deposition were mapped on the ground and it was possible to estimate
the probability of each type of behavior as a function of slope gradient and the width, or lack
of, the confining channel. For this project landslide tracks are mapped from aerial
photographs and the precise extent of scour, transitional flow, and deposition are unknown.
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To estimate topographic controls on the location of these zones, I use the initiation points as a
set of known scour locations, and the ending points as a set of known deposition locations.
The transition from scour to deposition along debris flow tracks is observed to vary with slope
gradient and channel confinement (Benda and Cundy 1990, Fannin and Rollerson 1993).
Miller and Burnett used a “width-weighted slope”, SW, to characterize scour and depositional
zones:
SW =
Sinθ
W
(11)
Values for the initiation and ending points of all observed upslope landslides are plotted in the
cumulative curves shown in the upper graph of Figure 8. Over any increment of SW, the
probability of scour or deposition can be estimated from the proportion of initiation (scour)
and ending (deposition) points. These proportions can be obtained using binned values or
from the slope of the cumulative distributions:
PS =
dn S
dSW
 dn S dn D

+
 dSW dSW



and PD =
dn D
dSW
 dn S dn D

+
 dSW dSW



(12)
Here PS is the probability of scour and PD the probability of deposition, nS is the number of
scour (initiation) points and nD is the number of deposition (ending) points. The derivatives
dnS/dSW and dnD/dSW are estimated from regression of a logistic equation to the curves in
Figure 8. The resulting PS and PD values are shown in the lower graph of Figure 8. I plotted
points laying in areas of OPEN and FOREST land-cover types separately. These plot as
distinctly different curves, with scour and deposition points both offset towards larger SW
values (steeper slopes, less confined channels) under the FOREST cover types. Similar results
were obtained by Miller and Burnett (in review) using field-mapped landslide and debris-flow
tracks from coastal Oregon.
It is useful to consider landslide runout in terms of the accumulated and deposited volume
(Cannon and Savage 1988, Cannon 1993): landslides continue downslope until all
accumulated sediment is deposited. With zones of scour and deposition delineated by Eqn. 12,
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we can make estimates of relative volume. Scoured volume correlates with runout length
(Benda and Cundy 1990, Fannin and Rollerson 1993, May 2002), so landslide volume should
be proportional to cumulative runout:
VS = ∑ (PS L )
(13)
Here VS, considered proportional to scoured volume, is the sum of scour probability
multiplied by pixel length L along the runout track. Similar reasoning is used to define VD,
considered proportional to deposited volume:
VD = ∑ (aVS1 3 LWPD )
(14)
The volume deposited in a pixel is equal to mean deposit depth, estimated as proportional to
VS1/3 following arguments of Iverson for lahar deposits (1998), multiplied by the pixel length
L and channel width W. This volume is multiplied by the probability of deposition and the
total is summed over all pixels traversed by the landslide to provide an estimate of total
deposited volume VD. Both VS and VD vary continuously along the runout path. When the
volume deposited equals the volume scoured, VS should equal VD, which should signify the
end of the runout track. In Figure 9 the distribution of VD/VS values is shown for all upslope
landslide end points, excluding those that stopped at tributary junctions. The constant of
proportionality a in Eqn. 14 is adjusted to set the modal value to zero. Some landslides
continue further than expected; some stop short. We can use this set of observations to
estimate the probability that a landslide will stop as a function of the value of VD/VS, as shown
in the lower graph of Figure 9. The VD/VS values are plotted against VS, with curves of the
form ±LOG(a1 + a2VSa3) defining the envelope all points. Along any potential runout track, if
the VD/VS value falls below the lower envelope, it is likely that very little of the landslide
debris has deposited and the probability that the landslide continues is considered to be one. If
the value of VD/VS falls above the upper envelope, it is likely that most of the debris has
deposited and the probability of continuing is considered to be zero. In between, the
probability of continuing is considered to vary linearly from one to zero.
Debris flow deposits are also commonly found where a small, steep tributary enters a larger
river valley at a sharp angle (Benda and Cundy 1990). The probability that a debris flow
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June 8, 2004
continues through a tributary junction is estimated as a function of the junction angle.
Conservation of momentum also indicates that the probability of continuing through a
tributary junction will vary with the volume of the debris flow and with the gradient and
width of the receiving valley floor. These relationships are confirmed with this data set, as
shown in Figure 10. Debris flows tend to continue through low-angle junctions into steep,
narrow channels and tend to stop at high-angle junctions into low-gradient, wide valley floors.
Likewise, the larger the debris flow, based on the difference between VS and VD, the more
likely it is to continue through the junction. These observations are used to estimate the
probability of continuing through a junction, based on the proportion of “stopping” and
“continuing” points (Figure 10) within binned ranges of each of these three variables (VS –
VD, junction angle, and probability of deposition PD in the receiving channel).
The observations described above characterize landslide and debris flow tracks in terms of
measurable terrain attributes. The probability of scour or deposition along the track is a
function of surface gradient, channel confinement, and land cover. The probable length is a
function of the cumulative length of scour and deposition from the initiation point. The
probability of stopping at tributary junctions varies with junction angle, cumulative upslope
scour and deposition length, and the gradient and width of the receiving channel. All of these
attributes can be estimated from the DEM and land-cover data, and all functional relationships
were calibrated to these data. This allows us to estimate the probability of landslide delivery
from any point in the DEM to any other point in the DEM. This probability varies
continuously along any potential runout track in response to topography and land cover. A
change in land cover results in a change in predicted runout length, with OPEN cover groups
favoring longer runout distances.
Because delivery probability depends on the specific travel path from the initiating pixel, the
value differs for delivery to different portions of the channel network. Figure 11 shows
calculated probabilities for delivery to any channel reach with no downstream gradients
greater than 10%. Compared to the results shown in Figure 12, the probability for delivery to
channel reaches with no downstream gradient exceeding 2%, we find that in many places the
probability is greatly reduced. Many of the debris flows that would likely reach a channel of
10% stop prior to reaching a channel of 2%.
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June 8, 2004
This concludes description of methods and results for upslope landslides, although
applications of these models and results will be addressed in the discussion section of the
report.
Stream Side Landslides
Stream-side landslides entail about half of the mapped landslides, both in number and in
surface area. They differ from upslope landslides in two primary respects: 1) they may be
triggered by stream erosion, and 2) they have a 100% probability of delivery to a stream
channel, which are typically located in the lower-gradient, fish-bearing portion of the channel
network. For these reasons they are analyzed separately. Below I define a different
topographic index for characterizing topographic controls on stream-side landsliding, and
present results for landslide density.
Consideration of the factors influencing stability of stream-adjacent slopes (Miller 1995)
suggests that an index to resolve topographic controls on stream-side landslides should be
sensitive to slope steepness, to cutting of the slope toe by stream-bank erosion, and to distance
from a stream channel. A simple measure of slope steepness is given by SV = YV/XV, where YV
is the vertical distance from a point on the hillslope to the valley floor, and XV is the
horizontal distance. To assess sensitivity to cutting of the slope toe, consider that bank erosion
reduces XV by an amount dx. The corresponding change in SV is:
∆SV =
 dx 
YV
Y
YV dx

− V =
= SV 
X v − dx X V
X V ( X V − dx )
 X V − dx 
(15)
Equation 15 provides an index, ∆SV, with all the desired quantities. It requires, however,
specification of the magnitude of bank erosion, dx. Following a long tradition in fluvial
geomorphology, I make dx a power function of drainage area A:
dx = 5.0*A0.3
(16)
with the constants set to give what I think are reasonable amounts of potential bank
undercutting, which can be substantial, considering the extent of channel migration observed
at the Steelhead Haven landslide (Miller and Sias 1998, Drury 2001).
15
June 8, 2004
To determine values for YV and XV, valley-floor pixels are delineated based on height above
the channel and surface gradient (Miller 2003). Both YV and XV are determined in reference to
distance from the valley floor. Using Equations 15 and 16, a value for ∆SV is calculated for
every pixel that is not on the valley floor. Digitized stream-side landslide locations are
overlain on the resulting grid, from which landslide density can be determined as a function of
the topographic index using the same methods described previously (Eqn.s 4 through 10). An
important difference here, however, is that density is given in terms of landslide surface area
per unit basin area (excluding valley floors), rather than in numbers of landslides per unit
basin area. Again, analyses were done for each group of geology and landcover types,
excluding roads, because there were essentially no roads that crossed streamside landslides. In
this case, substantial differences were resolved in the topographic weighting function between
different geology classes (Figure 13). Higher landslide densities occur at low ∆SV values in
deep glacial sediments. For both geologic groups, topographic weighting first increases with
increasing ∆SV, indicating greater landslide density associated with steeper slopes, and then
decreases, indicating that very steep slopes tend to have few observable active landslides. The
resulting topographic weighting values are shown for the basin in Figure 14. Note, however,
that these results may not accurately reflect stream-side landslide potential along portions of
the valley wall protected by revetments or other means of bank-protection, since these areas
are precluded to some extent from toe-slope erosion.
The DeForest Creek landslide was not included in this analysis. Because of the extensive
erosion associated with this landslide complex, the topography controlling currently active
portions of the landslide are no longer represented by the contours in the USGS 7.5-minute
quadrangle from which the DEM was derived. Although I am reluctant to exclude such a
major landslide, it’s inclusion in the analysis substantially biases the topographic weighting
function to lower ∆SV values, which is not representative of results for the rest of the basin,
where the current topography is more accurately represented.
Resulting landslide densities are reported in Table 3 below and in Figure 15. Differences
between geology and cover classes parallel those found for upslope landslides, with lower
observed densities in the FORESTED land-cover group. Likewise, deep glacial sediments
16
June 8, 2004
exhibit a much higher density in both FORESTED and OPEN land cover than found for other
areas.
Table 3. Stream-Side Landslide Densities
Density
Total Area
(m2/km2)
(m2)
1,357
256,000
Glacial-Forested
380
111,400
Bedrock-Open
312
103,600
Bedrock-Forested
172
141,600
Cover
Glacial-Open
APPLICATIONS
Landslide Hazards
These data sets and analysis tools provide the opportunity to examine landslide hazards from
a variety of different contexts. I’ll present an example here, but ultimately the hazard must be
defined in the context of what is at risk. Traditionally, landslide hazards are considered in
terms of the potential for a landslide to strike the point of interest, which in this case are
stream channels. This potential (based on the 2001 photo set) can be calculated as the product
of the initiation and delivery probability. Because delivery probability depends on the location
of the receiving point, hazards defined this way vary depending on what portion of the
channel system is examined.
A combined probability for upslope and stream-side landslides can be defined as follows:
PLS = 1 – (1-PIPD)*(1-PSS)
(17)
Here PLS is the probability that a landslide from a hillslope pixel has occurred and has
delivered to a stream channel. PLS is defined for every pixel of the DEM. PI is the initiation
probability for the pixel; PD is the probability of delivery from the pixel, and PSS is the
probability that a stream-side landslide occupies the pixel, with an inferred delivery
17
June 8, 2004
probability of one. As seen from Figures 11 and 12, the delivery probability PD varies
depending on what portion of the channel network we are concerned about. Using the 10%
gradient or less portion of the channel network for upslope landslides and all channels for
streamside landslides, Eqn. 17 gives a map of probabilities as shown in Figure 16. Details of
this map will change depending on which channels probabilities are defined for.
In many cases, the hazard of interest is that posed by a proposed land use. For Washington
State, the State Environmental Protection Act (SEPA) requires determination of the
“likelihood” that a proposed forest practice will cause movement on potentially unstable
slopes or landforms and the “likelihood” of delivery of sediment to public resources. These
tools allow quantification of that likelihood to the extent that it is resolved by the available
topographic data. The landslide probability defined by Equation 17 depends explicitly on land
cover at both the initiation site and down slope. The change in probability of landslide
occurrence, as inferred from the 2001 data, and the change in delivery probability associated
with timber harvest or road building can be quantitatively estimated.
Watershed Processes
These methods can be used to quantify and visualize a variety of landslide-related watershed
processes. I’ll present several examples to illustrate the types of applications these analyses
may be used for.
Debris-Flow-Prone Channels
With estimates of the probability for initiation and delivery, it is feasible to classify channels
in terms of the potential for debris flow impacts. This is one of the applications of the model
used by the Coastal Landscape Analysis and Modeling project, a collaborative effort of the
Forest Service and Oregon State University (CLAMS, http://www.fsl.orst.edu/clams/). In the
Oregon Coast Range, and elsewhere, debris flows are an important habitat-forming process
(Everest and Meehan 1981, Benda 1990, Reeves et al. 1995, Benda et al. 2003), so delineating
the extent of the channel network that is debris-flow prone is an important step in defining
ecosystem dynamics for these landscapes. Figure 17 shows the estimated probability for
debris-flow delivery (scour and/or deposition) for third and higher-order channels in the
Stillaguamish basin. Results indicate that debris-flow-prone channels are relatively rare in the
18
June 8, 2004
basin, and restricted to narrow valley floors or headwater channels. Even so, we can identify
areas where upslope landslides may be have local effects (Brummer and Montgomery 2003),
as indicated by the blowup shown in Figure 17.
Sediment Yield
The landslides identified on the 2001 aerial photographs provide an indication of landsliderelated sediment fluxes over a period spanning several years. We can use the derived
probabilities for landslide initiation and delivery to estimate spatially distributed rates of
landslide-delivered sediment to the channel system. Again, because the probability for
delivery varies with channel extent, this rate depends on the portion of the channel network
included. Landslide delivery to first and second-order channels may be very large, but most of
this sediment never reaches larger, low-gradient channels, at least not as landslide debris.
Most is deposited in fans and terraces through the low-order portion of the channel network.
Some portion is eventually transported downstream by fluvial processes, or carried
downstream after incorporation into a long-runout debris flow.
We can estimate upslope landslide sediment yield based on the probable cumulative length of
landslide and debris flow scour. For every hillslope pixel, the probability PT that it is traversed
by a landslide or debris flow from upslope is given by
PT = 1 – Π(1-PIPD)
(18)
where the product is made over all upslope landslide source pixels and delivery probability PD
refers to a channel segment of interest. The sum ΣPSPTL over all pixels draining to a channel
reach, where L is flow length across each pixel and PS is the scour probability (Eqn. 12),
provides an estimate of cumulative landslide and debris-flow scour length delivered to the
channel. Field surveys from western Oregon suggest that the sediment volume available for
debris-flow erosion from low-order channels is on the order of 10m3/m (Benda and Cundy
1990, May 2002). So, the cumulative length of debris flow scour translates roughly to a
sediment volume. Summed over channel length and divided by drainage area, it provides
topography, geology, and land-cover estimate of upslope landslide sediment yield, which as
described above, varies depending on what portion of the channel network is examined.
19
June 8, 2004
Figure 18 shows results based on the 2001 aerial photographs for landslide and debris-flow
delivery to channels of gradient 10% or less.
We can perform the same exercise for stream-side landslides, although for these we simply
sum the probability that a pixel is within a landslide scar, assuming that all stream-side
landslides deliver sediment to stream channels. This sum gives the cumulative probable
surface area of stream side landslides, which when divided by drainage area gives sediment
yield. Results are shown in Figure 19. Assuming a mean depth of one meter for stream-side
landslides, I estimate sediment yields two orders of magnitude greater than those from
upslope landslides. The one-meter depth estimate may be a bit large, since many of the
mapped landslides represent persistent scars, whose contribution consists of ravel from the
exposed surface, rather than full-fledged failure of a soil column. Nevertheless, it appears that
stream-side landslides play a much larger overall role in sediment supply than upslope
landslides, although upslope landslides can still be of major importance locally (Figure 17).
The lower image in Figure 19 shows estimated sediment yield from mapped landslides,
assuming a mean depth of delivery of one meter. When averaged over the basin, the yield is
the same as that in the upper image, but the distribution of sediment inputs is much more
heterogeneous, with channels across the basin carrying a very large range of sediment fluxes
(Figure 20). This illustrates an important aspect of mass-wasting processes: sediment supply
is punctuated in space and time (Reeves et al. 1995, Benda and Dunne 1997). Single
landslides can play a dominant role, as demonstrated by the DeForest Creek, Steelhead
Haven, and Gold Basin landslides in the Stillaguamish basin (Collins et al. 1994, Collins
1997). Their effects are transient, and their occurrence unpredictable (stochastic), leading to
shifts in the location of suitable habitat within the basin (Reeves et al. 1995).
Relationship to Fish Habitat
Variable sediment production from landsliding initiates a variety of downstream effects
(Nakamura et al. 2000, Gomi et al. 2002), many of which can be detrimental to fish
populations. In the Stillaguamish basin, these include widening of the active channel, loss of
pools, and fining of channel-bed texture, all of which are exacerbated where other factors
have removed large trees from riparian forests (Collins et al. 1994, Collins 1997). The data
and analyses tools developed for this project may aid in assessing landslide impacts on fish
20
June 8, 2004
habitat. The topography and channel-network structure of the basin provide an intrinsic spatial
template that allows for a suite of different habitat types. Within any specific reach, the type
of habitat available is set by factors such as channel size, gradient, and valley constraint. The
quality of that habitat (e.g., the number and depth of pools) can vary over time in response to
changing sediment fluxes and woody debris loading. For a channel type, habitat quality and
potential fish productivity can be estimated to a certain degree in terms of sediment and
woody-debris loading (e.g., Nickelson and Lawson 1998). By juxtaposing channel types with
sources of sediment supply and riparian condition, it should be feasible to estimate potential
fish productivity throughout the basin. We could then, for example, identify those channels
with a high potential for increased production and those at high risk for decreased production.
To illustrate the idea, I use a simple index of intrinsic habitat potential (Burnett et al. 2003) to
delineate potential habitat types. This index is based on three channel attributes: gradient,
mean annual flow, and valley confinement. Its value varies from zero, representing no
available habitat, to one, representing potentially high-quality habitat (oriented particularly
towards rearing habitat). The index has been calibrated to the distribution of coho salmon
(Oncorhynchus kisutch) and steelhead trout (Oncorhynchus mykiss) in coastal Oregon
streams. By multiplying available habitat area, based on estimated channel and flood-plain
widths, maximum observed smolt densities, and index values reach-by-reach and integrating
over all channels, Oregon Department of Fish and Wildlife have estimated maximum coho
productivity, based on cannery records, remarkably accurately for Oregon Coast Range basins
(T. Nickelson, personal communication). I use this only as an example for delineating habitat
types, since other, more basin-specific measures of potential habitat quality may be available
for the Stillaguamish basin.
Intrinsic potential index values were calculated using a regression of historical channel width
to drainage area from Pess et al. (1999), as reported by Pelletier and Bilhimer (2003), a
regression to mean-annual flow estimates from Sinclair and Pitz (1999), with drainage area
and valley width estimated from the DEM (Miller 2003). Index values are shown for thirdand higher-order channels in Figure 21. These illustrate use of simple terrain attributes
(drainage area, gradient, valley width) to delineate potential habitat types specific to different
species. Potentially high-quality coho habitat tends to be located in unconstrained, low-
21
June 8, 2004
gradient streams, whereas potentially high-quality steelhead habitat tends to be located in
more constrained, higher-gradient streams.
By overlaying this measure of potential habitat quality with the estimated sediment yield from
stream-side landslides, we can highlight channels where high sediment fluxes may be
hindering fish production. Multiplying the intrinsic potential index value by the sediment
yield gives the results shown in Figure 22. Alternatively, we could also highlight those
channels with both a high intrinsic potential and low sediment yields.
CONCLUSIONS
Newly available data and analysis methods offer capabilities for assessing landslide hazards
that can supplement and enhance traditional methods, which focus primarily on identification
of potentially unstable slopes and landforms. Digital data sets and computer-aided analyses
can be used to quantify terrain and land-cover controls on landslide susceptibility. Flow paths
over digital elevation models show how landslide debris is routed through the landscape, with
which runout lengths can be estimated and depositional sites identified. These capabilities
greatly expand the domain of landslide-related issues that can be examined and quantified.
I have described analysis methods and model calibration for the Stillaguamish basin.
Demonstration of the utility of this approach awaits application of the models to specific
questions. For example, because landslide probabilities are tied to land cover, proposed
activities (Forest Practice Applications) can be evaluated in terms of the relative change in the
probability for landslide occurrence and delivery to a stream channel. The accuracy of such
estimates depends on how well local topography is represented by available data sources, and
awaits verification, but this capability provides a quantitative method for screening and
evaluating proposed activities. The ability to delineate landslide process domains (Figure 17)
and to quantify landslide-derived sediment fluxes (Figure18 and 19) can provide information
useful to other types of planning efforts. These analyses, when considered in the context of
other information such as the distribution of fish habitat, may aid restoration and conservation
planning.
The models provide specific, testable hypotheses (such as potential location of landslide and
debris-flow deposits shown by Figure 17) that can be evaluated against field observations.
22
June 8, 2004
Likewise, the data and models are digital, so that data can be updated as new sources become
available. These aspects allow this methodology to evolve as data constraints are identified
and as new information (e.g., additional landslide inventories) is collected.
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26
Meters
0
Figure 1. Portion of an orthophoto (Mt. Higgins quadrangle) showing
a section of upper Deer Creek with mapped landslides. Streamside
landslides are shown in green and upslope landslides in yellow.
500
Kilometers
0
5
10
GLACIAL-OPEN
GLACIAL-FORESTED
GLACIAL-ROADS
BEDROCK-OPEN
BEDROCK-FORESTED
BEDROCK-ROADS
Figure 2. Stillaguamish basin geology and land-cover groups.
PIN/B tanq = 1
PIN/B tanq = 2
PIN/B tanq = 3
Figure 3 Topographic index I* shown as a function of slope gradient for different
PIN/Btanq values. Lower I* values correspond to lower slope stability.
Landslide initiation points
Area
Figure 4. The proportion of basin area (including only areas with slope gradients
greater than 25%) and mapped upslope landslide initiation points as functions of
the topographic index I* defined in Eqn. 3. The lower graph shows the proportion
of area plotted as a function of the proportion of area with increasing I*, using
values shown in the upper graph. Results for other potential index functions are
also shown. The index defined by Eqn. 3 requires the least area to include the
greatest number of landslides.
Topographic Weighting w T
120
100
80
60
40
20
0
0.5
1
1.5
2
Topographic Index I*
Figure 5. Topographic weighting value, wT, as a function of the topographic
index, I*, for upslope landslide initiation points.
Kilometers
0
5
10
Topographic
Weighting
0-1
1-3
3-5
5-7
7-9
>9
Meters
0
Figure 6. Topographic weighting of upslope landslide density.
500
Upslope Landslide Density (#/sq km)
2.5
2.0
1.5
1.0
0.5
0.0
GlacialOpen
GlacialForest
GlacialRoads
Figure 7. Upslope landslide densities.
BedrockOpen
BedrockForest
BedrockRoads
100%
90%
80%
Proportion
70%
60%
50%
40%
30%
Scour - OPEN
Deposition - OPEN
Scour - FORESTED
Deposition - FORESTED
20%
10%
0%
0
0.2
0.4
0.6
0.8
Sw
100%
90%
80%
Probability
70%
PD
60%
OPEN
FORESTED
50%
40%
30%
PS
20%
10%
0%
0
0.2
0.4
0.6
0.8
Sw
Figure 12. Scour and depositional zones along upslope landslide runout tracks,
based on the channel-width-weighted slope gradient, SW (Eqn. 11). The upper graph
shows the cumulative distribution of initiation (scour) and ending (deposition) points
for each mapped landslide track, divided by land-cover type. Dark curves show
logistic regression to these points. The lower graph shows resulting probabilities for
scour (PS) and deposition (PD).
100%
Upslope Landslide End Points
90%
Proportion
80%
70%
60%
50%
40%
30%
20%
10%
0%
-3
-2.5
-2
-1.5
-1
-0.5
Log(VD/VS)
0
0.5
1
2
Log(Vd/Vs)
1
0
-1
-2
-3
-4
0
100
200
300
400
500
600
700
Vs
Figure 9. Observations used to estimate probable landslide and debris flow runout
length as a function of conditions along the cumulative upslope runout track. The
upper graph shows the cumulative distribution of VD/VS, the ratio of estimated
deposited volume VD to scoured volume VS, for all landslide-track end points,
excluding those that stopped at tributary junctions. The lower graph shows the same
values plotted against scour volume. The curves envelope all points and provide
bounds between which the probability that a landslide continues varies from one at
and below the lower curve to zero at and above the upper curve.
90
Junction Anglea
80
70
60
50
40
30
20
Continued
Stopped
10
0
-600
-400
-200
0
200
400
600
VS - V D
1
0.9
0.8
0.7
0.6
PD 0.5
0.4
0.3
0.2
Continued
Stopped
0.1
0
0
20
40
60
Tributary Junction Angle (deg)
80
Figure 10. Observations used to estimate the probability that a debris flow continues
through a tributary junction. The upper graph shows the value of the relative debris
flow volume, based on the difference between VS and VD, and the junction angle for
all tributary junctions intersected by debris flow (or landslide) tracks in the data set.
These are identified as to whether they continued through the junction, or stopped at
the junction. The distribution of points indicates that both relative volume and junction
angle influence the propensity for stopping, with a higher proportion of stopping points
at lower relative volumes and higher junction angles. The lower graph plots these
points in terms of the tributary junction angle and the depositional probability PD
(Eqn. 12), which is a function of receiving channel gradient and valley floor width.
Here a very strong relationship with PD is found. Most stopping points are at high PD
values (low-gradient, wide valley floors), whereas continuing points are equally
distributed throughout.
Kilometers
0
5
10
Delivery
Probability
0 - 25%
25 - 50%
50 - 75%
75 - 100%
Meters
0
500
Figure 11. Probability of delivery from hillslope pixels to stream channels of
gradient 10% or less.
Kilometers
0
5
10
Delivery
Probability
0 - 25%
25 - 50%
50 - 75%
75 - 100%
Meters
0
500
Figure 12. Probability of delivery from hillslope pixels to stream channels of
gradient 5% or less.
Landslide Area
100%
90%
GLACIAL - OPEN
80%
GLACIAL - FORESTED
70%
GLACIAL - OPEN, no DeForest Cr.
60%
BEDROCK - OPEN
50%
BEDROCK - FORESTED
40%
30%
20%
10%
0%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Basin Area
Area
100%
GLACIAL
Wt
10
Area
100%
9
90%
90
8
80%
80
7
70%
70
6
60%
60
50%
5
50%
50
40%
4
40%
40
30%
3
30%
30
20%
2
20%
20
10%
1
10%
10
0%
0
90%
80%
Basin Area
Landslide Area
Topographic Weighting
70%
60%
0
0.5
1
I* (SV)
1.5
2
BEDROCK
Wt
100
0%
0
0
0.5
1
I* (SV)
1.5
Figure 13. Cumulative distributions of landslide and basin area, with increasing
topographic index (DSV) for geology and land-cover groups. The upper graph shows
substantial difference between the distributions for GLACIAL and BEDROCK geologic
groupings, but no significant difference between cover types. Topographic weighting was
therefore determined separately for the two geologic groups, as shown in the bottom
figures.
2
Kilometers
0
5
10
Topographic
Weighting
1-3
3-5
5-7
7-9
>9
Figure 14. Topographic
weighting for stream-side
landslides. The valley
floor is shown by the blue
shade. Delineation of
valley-floor pixels was
extended to all channels,
including first order.
Streamside Landslide Density (m 2/km2)
1400
1200
1000
800
600
400
200
0
Glacial-Open
Glacial-Forested
Bedrock-Open
BedrockForested
Fikgure 15. Stream-side landslide density for the geology and land-cover groups.
Kilometers
0
5
10
Landslide
Probability
>0 - .002
.004
.006
.008
>.008
Figure 16. Landslide
probability based on a
combination of initiation
and delivery probabilities
(Eqn. 17). For upslope
landslides, the probability
for delivery to 10% or
less channels was used.
Meters
0
500
Kilometers
0
5
10
Debris Flow
Probability
0
> 0 - 0.002
0.004
0.006
0.008
0.010
> 0.010
Figure 17. Probability
for debris flow channel
impacts, either scour or
deposition. The reported
value is the probability of
finding a landslide track
in similar channels in the
2001 data set.
Meters
0
500
Kilometers
0
5
10
Upslope Landslide
3
2
Sediment Yield (m /km )
0
>0 - 2
4
6
8
10
>10
Figure 18. Sediment yield from upslope
landslides and debris flows that deliver to
channels of gradient 10% or less, based on recent
landslides observed on 2001 aerial photographs.
Predicted
Kilometers
0
5
10
Stream-Side Landslide
3
2
Sediment Yield (m /km )
0
> 0 - 200
400
600
800
1000
>1000
Actual
Figure 19. Stream-side landslide sediment yield,
based on landslides observed in the 2001 aerial
photos. Upper graph shows calibrated model
reults, lower graph shows estimate based on
mapped landslides
Approximate Sediment Yield from Stream-Side Landslides (2001)
Cumulative Channel Length (km)
600
500
400
300
200
100
0
0.1
1
10
100
1000
10000
2
Metric tons/km
Figure 20. Cumulative distribution of estimated mean annual sediment yields
from stream-side landsliding over third and higher-order channels. Sediment
yield is calculated from the surface area of active landslides mapped from the
2001 aerial photographs, assuming a 1-meter deep failure surface and that
landslides observed on the photos represent 10 years of landslide activity.
Sediment volume was converted to metric tons using an assumed bulk density of
1800 kg/m3.
Steelhead
0
Km
10
Intrinsic
Potential
Coho
Figure 21. Intrinsic habitat potential, from poor
(zero) to high (one) for steelhead trout and coho
salmon, based on estimated mean annual flow,
channel gradient, and channel constraint.
Steelhead
Kilometers
0
5
10
Low sediment, or little habitat
High sediment and good habitat
Coho
Figure 22. Juxtaposition of potentially highquality habitat and high sediment fluxes
from stream-side landsliding. Red color
indicates channels with potentially high
habitat quality subject to high sediment
fluxes.

Landslide Hazards - Stillaguamish Tribe

Transcription

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